Course Syllabus

Course Description:

One of the greatest scientific triumphs in the 21st century is the deep understanding of the fundamental building blocks of matter and their interactions at the zepto scale. The Higgs Boson discovery in 2012 completes the last missing pieces of this theory, known as the Standard Model.

This is not end of the journey, however. Scientists believe that invisible dark matter and dark energy make up 95% of the total energy and mass of the universe. These theories suggest frontiers of physics beyond the Standard Model. The revolution of artificial intelligence (AI), in particular deep learning, offers a brand-new way of thinking to investigate the data and unravel deep mysterious of the universe.

What the Course Covers:

This course introduces you to cutting-edge quantum theory and how it explains some of the biggest puzzles in the universe. We’ll look at how state-of-the-art experiments with the Large Hadron Collider (LHC) can be used as tools to examine these groundbreaking theories and hunt for evidence of dark matter. You’ll get an overview of AI algorithms and how they are applied to the LHC data analysis. You’ll also learn about the latest research efforts of physicists at the UW to advance our understanding of quantum theory.

Course style

The course will be in-person. Before each class, students will study pre-lecture video/documents and complete pre-lecture quiz. The in-person lecture will be extensions based on the pre-lecture materials.

Who Should Attend?

Students interested in the different aspects of quantum theory and how AI can be used to advance discoveries. Knowledge of high school physics is recommended but not required.

Textbooks:

There is no official textbook in this class. However, there are two recommended reference materials that I'll follow closely for the lecture.

• Quantum Universe
  • Particle Adventure - basic introduction of particle physics, accelerators and detectors
  • Physics for the 21st Century, Annenberg Lerner - extension materials of this course
  • Einstein on-line - relativity and more
• Artificial Intelligence
  • Python Programming and Numerical Methods: A Guide for Engineers and Scientists  by Berkeley
  • Machine Learning — Andrew Ng, Stanford University [YouTubeLinks.]
  • Neural Networks and Deep Learning (Links to an external site.) by Michael Nielsen (concepts and examples; online book)

Grading Policy:

• Credits: 5
• Pre-Lecture Quiz (due 10:30 pm the day before the lecture) and in-class participation: 16%
  • The least 2 scores will be excluded from the final grade calculation
  • Allowed attempts: 3
• Homework (due 11:59 pm Mon) and oral report
  • homework I: 15%
  • homework II: 15%
  • homework III: 15%
  • homework IV: 15%
• Student Symposium Oral presentation 
  • elevator speeches): 8%
  • student symposium): 16%
• Late submission penalty  (20% off). Exempt of penalty has to be discussed prior to due time.

Schedule